Boosting With Structure Information in the Functional Space: An Application to Graph Classification
Boosting is a very successful classification algorithm that produces a linear combination of "Weak" classifiers (a.k.a. base learners) to obtain high quality classification models. In this paper, the authors propose a new boosting algorithm where base learners have structure relationships in the functional space. Though such relationships are generic, their work is particularly motivated by the emerging topic of pattern based classification for semi-structured data including graphs. Towards an efficient incorporation of the structure information, they have designed a general model where they use an undirected graph to capture the relationship of sub-graph-based base learners.